Marine networks are experiencing an expanding role in the global transportation of goods and are demanding an increasing energy resource while being a contributor to climate change-related emissions. This paper investigates the potential of hybrid energy source systems (HESS) that employ energy storage devices and peak power devices in a combination that is capable of providing average energy while recovering and managing the electrical power system transients. Moreover, the contribution of the energy storage device, or power buffer, may result in reduced rating for the main energy source, reducing system mass and volume while improving energy conversion efficiency. Crane system power flow is analyzed and energy saving calculated for a representative load cycle. Experimentally validated power-train models are presented, control strategies developed, and alternative energy/power storage devices in single and HESS configurations analyzed. While many papers discuss similar concepts for road vehicles, the application to port cranes has not been reported previously. Similarly, detailed design encompassing system losses, thermal management, component mass, volume, and system dynamic operation have not been reported previously. This paper develops procedures for the design of battery alone and battery-supercapacitor HESS that are shown to be different and independent of the optimization method chosen.

Electric traction machines have been applied to both industrial variable speed applications and electric vehicles. The induction machine (IM) is a potential candidate for traction machines due to the established industrial base. However, most of the existing studies on traction IM design rely on optimization algorithms or iterative calculation programs that subsequently give no clear understanding of the design requirements for field-weakening or extended speed operation. Hence, published procedures to guide IM design for traction applications are somewhat ad hoc to-date. This paper identifies the key IM design parameters required to achieve a traction characteristic within power supply constraints. As an example, a 120 kW electric vehicle traction machine is studied as a benchmark machine and then the proposed design procedure is employed to re-design the machine to improve its extended speed performance. Improvements in terms of efficiency and field-weakening capability are presented. Experimental results from the benchmark machine are used for validation of the subsequent simulation studies reported in this paper. Finally, the identified parameters are shown to be similar with brushless permanent magnet machines. Thus, a generalized machine design philosophy can be concluded.

The electric vehicle with passive hybridization of fuel cells and supercapacitors leads to lower cost and compactness due to the absence of DC/DC converters. This paper models such a vehicle and evaluates the energy efficiency of its powertrain system. The powertrain component losses, as functions of electric machine torque, speed and DC-link voltage, are modelled with a high level of detail which are verified against available test data. Compared to a pure fuel cell system, the fuel cell efficiency is higher when supercapacitors are introduced under pulse current load, and it is higher at lower current amplitude. As the pulse current frequency increases, the fuel cell efficiency also increases due to higher proportional current from the high-efficiency supercapacitors. A multiplicity of drive cycles is selected, divided into a low, middle, and high speed category to analyze the powertrain efficiency. The total powertrain energy efficiency varies between 53%-71% during propulsion for the studied drive cycles, whereas it is higher during braking ranging from 84% to 94%. The differences are closely related to the speed, acceleration, and DC-link voltage levels. The lower powertrain efficiency causes higher hydrogen consumption, leading to a reduced fuel cell efficiency at high speed, high acceleration and low DC-link voltage.

Road target detection is essential for enhancing vehicle safety, increasing operational efficiency, and optimizing user experience. It also forms a crucial part of autonomous driving and intelligent monitoring systems. However, current technologies face significant limitations in multi-level feature fusion and the accurate identification of key targets in complex data environments. To address these challenges, this paper proposes an innovative algorithmic model called BiFPN GAM SimC2f-YOLO (BGS-YOLO), aimed at improving detection performance. Initially, this paper employs the Bidirectional Feature Pyramid Network (BiFPN) to effectively integrate multi-level features. This integration overcomes the limitations in feature extraction and recognition found in existing target detection algorithms. Following this, this paper introduces the Global Attention Module (GAM), which markedly improves the efficiency and accuracy of extracting key target information in complex data environments. Additionally, this paper innovatively designs the SimAM-C2f (SimC2f) network, further advancing feature expressiveness and fusion efficiency. Experiments on the public COCO dataset demonstrate that the BGS-YOLO model significantly outperforms the existing YOLOv8n model. Notably, it shows a 7.3% increase in mean average precision (mAP) and a 2.4% improvement in accuracy. These results highlight the model’s high precision and swift response in detecting road targets in complex traffic scenarios. Consequently, the BGS-YOLO model has the potential to significantly enhance road safety and contribute to a considerable reduction in traffic accident rates.

DC-link voltage and temperature variations are critical issues when designing an electric vehicle (EV) traction system. However, systems are generally reported at fixed voltage and temperature and may not, therefore, be fully specified when considering the variation of these parameters over full vehicle operating extremes. This paper presents an assessment of power-train options based on the Nissan Leaf vehicle, which is taken as a benchmark system providing experimental validation of the study results. The Nissan Leaf traction machine is evaluated and performance assessed by considering dc-link voltage and temperature variations typical of an automotive application, showing that the system lacks performance as battery state of charge decreases. An alternative traction machine design is proposed to satisfy the target performance. The vehicle power-train is then modified with the inclusion of a dc/dc converter between the vehicle battery and dc-link to maintain the traction system dc-link voltage near constant. A supercapacitor system is also considered for improved system voltage management. The trade-offs for the redesigned systems are discussed in terms of electronic and machine packaging, and mitigation of faulted operation at high speeds.